Netflix Analysis
Loading Packages
knitr::opts_chunk$set(echo = TRUE, message = FALSE)
#load required packages
library(tidyverse)
library(dplyr)
library(ggplot2)
library(plotly)
library(rworldmap)Data Description
For the purpose of this project, a Netflix dataset sourced from Kaggle has been used.
df <- read.csv("netflix_titles.csv", stringsAsFactors = F) # read data
summary(df) # summary statistics## show_id type title director
## Length:7787 Length:7787 Length:7787 Length:7787
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## cast country date_added release_year
## Length:7787 Length:7787 Length:7787 Min. :1925
## Class :character Class :character Class :character 1st Qu.:2013
## Mode :character Mode :character Mode :character Median :2017
## Mean :2014
## 3rd Qu.:2018
## Max. :2021
## rating duration listed_in description
## Length:7787 Length:7787 Length:7787 Length:7787
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
## length(df$release_year) # number of observations## [1] 7787UniqueValue = function (x) {length(unique(x))} # shows number of unique values in each column
apply(df, 2, UniqueValue) # number of unique values in each column## show_id type title director cast country
## 7787 2 7787 4050 6832 682
## date_added release_year rating duration listed_in description
## 1566 73 15 216 492 7769sapply(df, function(x) sum(x == "")) # number of missing values## show_id type title director cast country
## 0 0 0 2389 718 507
## date_added release_year rating duration listed_in description
## 10 0 7 0 0 0The original dataset consists of 7787 rows and 12 columns. All columns excluding ‘show_id’ and ‘title’ had unique values less than 7787, which is the total number of rows of this Netflix dataset. This indicates presence of duplicates in those columns. Also, many blank - "" - values has been found in ‘director’,‘cast’,‘country’,‘date_added’, and ‘rating’ columns.
Cleaning all misleading values at once will lead to major data loss. In order to prevent data biases, pre-processing for this dataset will be done to a minimum. Cleaning will only be done on columns in use.
Visualization
In this section, only relevant columns will be pre-processed, then be used for creating visualizations. Pre-processing methodologies will be discussed under each analysis topic.
Netflix trends by year
This section will investigate the Netflix trend in their contents addition by year - regardless of contents release date. Note that a movie released in 2017 can be added on Netflix in 2020. How many movies and TV shows they add to their platform each year? Does Netflix treat movies and TV shows differently?
The ‘date_added’ column was originally in ‘month day, year’ format as character type. Therefore, it has been converted to numeric ‘year’ for yearly trend analysis. Missing values in ‘date_added’ also has been removed for the purpose of this analysis.
# To investigate yearly trends, I need to eliminate Month and Day data from 'date_added' column
head(df$date_added,5) # this column is originally in 'Month Day, Year' format## [1] "August 14, 2020" "December 23, 2016" "December 20, 2018"
## [4] "November 16, 2017" "January 1, 2020"class(df$date_added) # column class: character## [1] "character"df$date_added <- sub(".*(\\d+{4}).*$", "\\1", df$date_added) # convert date to year
head(df$date_added,5) # this column now only has year data## [1] "2020" "2016" "2018" "2017" "2020"# total number of movies and TV shows added to Netflix by year
year <- df %>%
filter(!(date_added == "")) %>% # remove missing values
group_by(date_added) %>%
summarise(count = n()) # number of movies and TV shows by year added to Netflix
year## # A tibble: 14 x 2
## date_added count
## <chr> <int>
## 1 2008 2
## 2 2009 2
## 3 2010 1
## 4 2011 13
## 5 2012 3
## 6 2013 11
## 7 2014 25
## 8 2015 88
## 9 2016 443
## 10 2017 1225
## 11 2018 1685
## 12 2019 2153
## 13 2020 2009
## 14 2021 117# draw a bar plot for number of contents added to Netflix by year
bar <- ggplot(year, aes(x = date_added, y = count, fill = date_added)) +
geom_bar(stat = "identity") +
geom_text(aes(label = count), vjust = -0.3) +
labs(x = "Year", fill = "Year", title = "Number of Contents added to Netflix by Year") +
theme(plot.title = element_text(hjust = 0.5)) # center title
bar
# Netflix trends - movie vs TV shows
group <- df %>%
filter(!(date_added == "")) %>%
group_by(type, date_added) %>% # group by content type and year added
summarise(count = n())
group## # A tibble: 24 x 3
## # Groups: type [2]
## type date_added count
## <chr> <chr> <int>
## 1 Movie 2008 1
## 2 Movie 2009 2
## 3 Movie 2010 1
## 4 Movie 2011 13
## 5 Movie 2012 3
## 6 Movie 2013 6
## 7 Movie 2014 19
## 8 Movie 2015 58
## 9 Movie 2016 258
## 10 Movie 2017 864
## # … with 14 more rows# create bar plot for number of contents added by year by content type
bar <- ggplot(group, aes(x = date_added, y = count, fill = type)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Netflix Trends by Year", x = "Year", "Type") +
theme(plot.title = element_text(hjust = 0.5)) # center title
bar
First bar plot shows the total number of movies and TV shows added on Netflix by year. After years of adding more contents each year, Netflix finally added less number of contents to its platform in 2020.
Second chart compares its trend in movies to that of TV shows. Although the number of movies added to Netflix outnumbers that of TV shows on Netflix, it started to show a decrease in 2020, whereas number of TV shows added has continued to increase. This may be an indication of Netflix focusing more on TV shows.
Movie vs TV show
This section will show total number of movies and TV shows on Netflix.
# create data frame for number of contents by type
type <- df %>%
group_by(type) %>%
summarise(count = n())
type## # A tibble: 2 x 2
## type count
## <chr> <int>
## 1 Movie 5377
## 2 TV Show 2410#all plots created afterwards will have title at center
#theme_update(plot.title = element_text(hjust = 0.5))
# create bar plot for Netflix contents by type
bar <- ggplot(type, aes(x = type, y = count, fill = type)) +
geom_bar(stat = "identity") +
geom_text(aes(label = count), vjust = -0.3) +
labs(title = "Netflix contents by type", x = "Type", fill = "Type") + # add title
theme(plot.title = element_text(hjust = 0.5)) # center title
bar
# create data frames each for movies and TV shows for further analysis
movie <- df[(df$type == "Movie"),]
tv <- df[(df$type == "TV Show"),]As shown in the chart above, the number of movies on Netflix is more than double the number of TV shows.
For further analysis on each content type, the dataset has been split down into two separate datasets based on the ‘type’ column.
Netflix Movie Analysis
The remaining of this project will examine movies on Netflix. The dataset ‘movie’ that has been created above will be used.
Top 10 Countries by number of movies on Netflix
- Bar plot
# basic information about movies on Netflix
length(unique(movie$country)) # country column has many duplicates## [1] 591head(movie$country, 50) # some movies has listed multiple countries separated by comma## [1] "Mexico" "Singapore"
## [3] "United States" "United States"
## [5] "Egypt" "United States"
## [7] "India" "India"
## [9] "United States" "Thailand"
## [11] "United States" "Nigeria"
## [13] "Norway, Iceland, United States" "India"
## [15] "United Kingdom" "India"
## [17] "India" "India"
## [19] "India" "United States"
## [21] "South Korea" "Italy"
## [23] "Canada" "Indonesia"
## [25] "Indonesia" "United States"
## [27] "Canada" "United States"
## [29] "Romania" "Romania"
## [31] "Spain" "Turkey"
## [33] "Iceland" "Turkey"
## [35] "Nigeria" "United States"
## [37] "United States" "United States"
## [39] "South Africa, Nigeria" "France"
## [41] "United States, South Africa" "Spain"
## [43] "Portugal, Spain" "United States"
## [45] "United States" "Indonesia"
## [47] "India" "United States"
## [49] "United States" "United States"# split comma separated country names
country <- unlist(strsplit(movie$country, ", ")) # spliy country names at comma
unique(country) # some country names has comma at the end## [1] "Mexico" "Singapore" "United States"
## [4] "Egypt" "India" "Thailand"
## [7] "Nigeria" "Norway" "Iceland"
## [10] "United Kingdom" "South Korea" "Italy"
## [13] "Canada" "Indonesia" "Romania"
## [16] "Spain" "Turkey" "South Africa"
## [19] "France" "Portugal" "Hong Kong"
## [22] "China" "Germany" "Argentina"
## [25] "Serbia" "Denmark" "Poland"
## [28] "Japan" "Kenya" "New Zealand"
## [31] "Pakistan" "Australia" "Taiwan"
## [34] "Netherlands" "Philippines" "United Arab Emirates"
## [37] "Brazil" "Iran" "Belgium"
## [40] "Israel" "Uruguay" "Bulgaria"
## [43] "Chile" "Colombia" "Algeria"
## [46] "Soviet Union" "Sweden" "Malaysia"
## [49] "Ireland" "Luxembourg" "Austria"
## [52] "Peru" "Senegal" "Switzerland"
## [55] "Ghana" "Saudi Arabia" "Armenia"
## [58] "Jordan" "Mongolia" "Namibia"
## [61] "Finland" "Lebanon" "Qatar"
## [64] "Vietnam" "Russia" "Malta"
## [67] "Kuwait" "Czech Republic" "Bahamas"
## [70] "Sri Lanka" "Cayman Islands" "Bangladesh"
## [73] "United States," "Zimbabwe" "Hungary"
## [76] "Latvia" "Liechtenstein" "Venezuela"
## [79] "Morocco" "Cambodia" "Albania"
## [82] "Nicaragua" "Greece" "Cambodia,"
## [85] "Croatia" "Guatemala" "West Germany"
## [88] "Poland," "Slovenia" "Dominican Republic"
## [91] "Nepal" "Samoa" "Bermuda"
## [94] "Ecuador" "Georgia" "Botswana"
## [97] "Iraq" "Vatican City" "Angola"
## [100] "Jamaica" "Kazakhstan" "Malawi"
## [103] "Slovakia" "Lithuania" "Afghanistan"
## [106] "Paraguay" "Somalia" "Sudan"
## [109] "Panama" "United Kingdom," "Uganda"
## [112] "East Germany" "Ukraine" "Montenegro"# remove leftover commas from country names
country <- gsub(",","", country)
unique(country)## [1] "Mexico" "Singapore" "United States"
## [4] "Egypt" "India" "Thailand"
## [7] "Nigeria" "Norway" "Iceland"
## [10] "United Kingdom" "South Korea" "Italy"
## [13] "Canada" "Indonesia" "Romania"
## [16] "Spain" "Turkey" "South Africa"
## [19] "France" "Portugal" "Hong Kong"
## [22] "China" "Germany" "Argentina"
## [25] "Serbia" "Denmark" "Poland"
## [28] "Japan" "Kenya" "New Zealand"
## [31] "Pakistan" "Australia" "Taiwan"
## [34] "Netherlands" "Philippines" "United Arab Emirates"
## [37] "Brazil" "Iran" "Belgium"
## [40] "Israel" "Uruguay" "Bulgaria"
## [43] "Chile" "Colombia" "Algeria"
## [46] "Soviet Union" "Sweden" "Malaysia"
## [49] "Ireland" "Luxembourg" "Austria"
## [52] "Peru" "Senegal" "Switzerland"
## [55] "Ghana" "Saudi Arabia" "Armenia"
## [58] "Jordan" "Mongolia" "Namibia"
## [61] "Finland" "Lebanon" "Qatar"
## [64] "Vietnam" "Russia" "Malta"
## [67] "Kuwait" "Czech Republic" "Bahamas"
## [70] "Sri Lanka" "Cayman Islands" "Bangladesh"
## [73] "Zimbabwe" "Hungary" "Latvia"
## [76] "Liechtenstein" "Venezuela" "Morocco"
## [79] "Cambodia" "Albania" "Nicaragua"
## [82] "Greece" "Croatia" "Guatemala"
## [85] "West Germany" "Slovenia" "Dominican Republic"
## [88] "Nepal" "Samoa" "Bermuda"
## [91] "Ecuador" "Georgia" "Botswana"
## [94] "Iraq" "Vatican City" "Angola"
## [97] "Jamaica" "Kazakhstan" "Malawi"
## [100] "Slovakia" "Lithuania" "Afghanistan"
## [103] "Paraguay" "Somalia" "Sudan"
## [106] "Panama" "Uganda" "East Germany"
## [109] "Ukraine" "Montenegro"# store the list as data frame
movie_country <- data.frame(country = country)
unique(movie_country$country) # it now has clean country names## [1] "Mexico" "Singapore" "United States"
## [4] "Egypt" "India" "Thailand"
## [7] "Nigeria" "Norway" "Iceland"
## [10] "United Kingdom" "South Korea" "Italy"
## [13] "Canada" "Indonesia" "Romania"
## [16] "Spain" "Turkey" "South Africa"
## [19] "France" "Portugal" "Hong Kong"
## [22] "China" "Germany" "Argentina"
## [25] "Serbia" "Denmark" "Poland"
## [28] "Japan" "Kenya" "New Zealand"
## [31] "Pakistan" "Australia" "Taiwan"
## [34] "Netherlands" "Philippines" "United Arab Emirates"
## [37] "Brazil" "Iran" "Belgium"
## [40] "Israel" "Uruguay" "Bulgaria"
## [43] "Chile" "Colombia" "Algeria"
## [46] "Soviet Union" "Sweden" "Malaysia"
## [49] "Ireland" "Luxembourg" "Austria"
## [52] "Peru" "Senegal" "Switzerland"
## [55] "Ghana" "Saudi Arabia" "Armenia"
## [58] "Jordan" "Mongolia" "Namibia"
## [61] "Finland" "Lebanon" "Qatar"
## [64] "Vietnam" "Russia" "Malta"
## [67] "Kuwait" "Czech Republic" "Bahamas"
## [70] "Sri Lanka" "Cayman Islands" "Bangladesh"
## [73] "Zimbabwe" "Hungary" "Latvia"
## [76] "Liechtenstein" "Venezuela" "Morocco"
## [79] "Cambodia" "Albania" "Nicaragua"
## [82] "Greece" "Croatia" "Guatemala"
## [85] "West Germany" "Slovenia" "Dominican Republic"
## [88] "Nepal" "Samoa" "Bermuda"
## [91] "Ecuador" "Georgia" "Botswana"
## [94] "Iraq" "Vatican City" "Angola"
## [97] "Jamaica" "Kazakhstan" "Malawi"
## [100] "Slovakia" "Lithuania" "Afghanistan"
## [103] "Paraguay" "Somalia" "Sudan"
## [106] "Panama" "Uganda" "East Germany"
## [109] "Ukraine" "Montenegro"# count country names appearing in the original dataset
movie_country <- movie_country %>%
group_by(country) %>%
summarise(count = n())
head(movie_country,10)## # A tibble: 10 x 2
## country count
## <chr> <int>
## 1 Afghanistan 1
## 2 Albania 1
## 3 Algeria 2
## 4 Angola 1
## 5 Argentina 64
## 6 Armenia 1
## 7 Australia 84
## 8 Austria 10
## 9 Bahamas 1
## 10 Bangladesh 3# top 10 countries by number of movies added to Netflix
bar <- movie_country %>%
arrange(desc(count)) %>% # most to least
slice(1:10) %>% # top 10 countries
ggplot(., aes(x=reorder(country,-count), y = count)) + # bar plot most to least
geom_bar(stat='identity') +
theme_classic() +
labs(x = "Country", title = "Top 10 Countries by number of movies on Netflix") +
geom_text(aes(label = count), vjust = -0.3) # add count labels
bar
United States has the most number of movies on Netflix.
- World Map
spdf <- joinCountryData2Map(movie_country, joinCode="NAME", nameJoinColumn="country")
mapParams <- mapCountryData(spdf,
nameColumnToPlot="count",
catMethod=c(0,1,3,5,10,50,100,300,500,1000,2500),
mapTitle = "Number of Movies added to Netflix",
addLegend = FALSE)
do.call(addMapLegend, c(mapParams, legendLabels="all", legendWidth=0.5))
#labelCountries()Continent-wise, Americas - North and South America - exhibits high number of Netflix movies overall.
Number of movies by Year
year 지날때마다 각 나라의 영화 개수 line graph - x = year, y = count, fill = country
genre_country <- movie %>%
mutate(listed_in = strsplit(as.character(listed_in), ", ")) %>% # separate genre by commas
unnest(listed_in) %>%
mutate(country = strsplit(as.character(country), ", ")) %>% # separate country name by commas
unnest(country) %>%
select("title","country","date_added","listed_in")
genre_country## # A tibble: 14,342 x 4
## title country date_added listed_in
## <chr> <chr> <chr> <chr>
## 1 7:19 Mexico 2016 Dramas
## 2 7:19 Mexico 2016 International Movies
## 3 23:59 Singapore 2018 Horror Movies
## 4 23:59 Singapore 2018 International Movies
## 5 9 United States 2017 Action & Adventure
## 6 9 United States 2017 Independent Movies
## 7 9 United States 2017 Sci-Fi & Fantasy
## 8 21 United States 2020 Dramas
## 9 122 Egypt 2020 Horror Movies
## 10 122 Egypt 2020 International Movies
## # … with 14,332 more rowsunique(genre_country$country) # some country names still have commas## [1] "Mexico" "Singapore" "United States"
## [4] "Egypt" "India" "Thailand"
## [7] "Nigeria" "Norway" "Iceland"
## [10] "United Kingdom" "South Korea" "Italy"
## [13] "Canada" "Indonesia" "Romania"
## [16] "Spain" "Turkey" "South Africa"
## [19] "France" "Portugal" "Hong Kong"
## [22] "China" "Germany" "Argentina"
## [25] "Serbia" "Denmark" "Poland"
## [28] "Japan" "Kenya" "New Zealand"
## [31] "Pakistan" "Australia" "Taiwan"
## [34] "Netherlands" "Philippines" "United Arab Emirates"
## [37] "Brazil" "Iran" "Belgium"
## [40] "Israel" "Uruguay" "Bulgaria"
## [43] "Chile" "Colombia" "Algeria"
## [46] "Soviet Union" "Sweden" "Malaysia"
## [49] "Ireland" "Luxembourg" "Austria"
## [52] "Peru" "Senegal" "Switzerland"
## [55] "Ghana" "Saudi Arabia" "Armenia"
## [58] "Jordan" "Mongolia" "Namibia"
## [61] "Finland" "Lebanon" "Qatar"
## [64] "Vietnam" "Russia" "Malta"
## [67] "Kuwait" "Czech Republic" "Bahamas"
## [70] "Sri Lanka" "Cayman Islands" "Bangladesh"
## [73] "United States," "Zimbabwe" "Hungary"
## [76] "Latvia" "Liechtenstein" "Venezuela"
## [79] "Morocco" "Cambodia" "Albania"
## [82] "Nicaragua" "Greece" "Cambodia,"
## [85] "Croatia" "Guatemala" "West Germany"
## [88] "Poland," "Slovenia" "Dominican Republic"
## [91] "Nepal" "Samoa" "Bermuda"
## [94] "Ecuador" "Georgia" "Botswana"
## [97] "Iraq" "Vatican City" "Angola"
## [100] "Jamaica" "Kazakhstan" "Malawi"
## [103] "Slovakia" "Lithuania" "Afghanistan"
## [106] "Paraguay" "Somalia" "Sudan"
## [109] "Panama" "United Kingdom," "Uganda"
## [112] "East Germany" "Ukraine" "Montenegro"length(unique(genre_country$country)) # number of unique country names## [1] 114unique(genre_country$listed_in) # clean; no leftover commas to be deleted## [1] "Dramas" "International Movies"
## [3] "Horror Movies" "Action & Adventure"
## [5] "Independent Movies" "Sci-Fi & Fantasy"
## [7] "Thrillers" "Documentaries"
## [9] "Sports Movies" "Comedies"
## [11] "Romantic Movies" "Movies"
## [13] "Music & Musicals" "LGBTQ Movies"
## [15] "Faith & Spirituality" "Children & Family Movies"
## [17] "Classic Movies" "Cult Movies"
## [19] "Stand-Up Comedy" "Anime Features"genre_country <- genre_country %>%
mutate(country = gsub(",","",country)) # remove commas by replacing them with space
genre_country## # A tibble: 14,342 x 4
## title country date_added listed_in
## <chr> <chr> <chr> <chr>
## 1 7:19 Mexico 2016 Dramas
## 2 7:19 Mexico 2016 International Movies
## 3 23:59 Singapore 2018 Horror Movies
## 4 23:59 Singapore 2018 International Movies
## 5 9 United States 2017 Action & Adventure
## 6 9 United States 2017 Independent Movies
## 7 9 United States 2017 Sci-Fi & Fantasy
## 8 21 United States 2020 Dramas
## 9 122 Egypt 2020 Horror Movies
## 10 122 Egypt 2020 International Movies
## # … with 14,332 more rowsunique(genre_country$country) # commas removed from country names## [1] "Mexico" "Singapore" "United States"
## [4] "Egypt" "India" "Thailand"
## [7] "Nigeria" "Norway" "Iceland"
## [10] "United Kingdom" "South Korea" "Italy"
## [13] "Canada" "Indonesia" "Romania"
## [16] "Spain" "Turkey" "South Africa"
## [19] "France" "Portugal" "Hong Kong"
## [22] "China" "Germany" "Argentina"
## [25] "Serbia" "Denmark" "Poland"
## [28] "Japan" "Kenya" "New Zealand"
## [31] "Pakistan" "Australia" "Taiwan"
## [34] "Netherlands" "Philippines" "United Arab Emirates"
## [37] "Brazil" "Iran" "Belgium"
## [40] "Israel" "Uruguay" "Bulgaria"
## [43] "Chile" "Colombia" "Algeria"
## [46] "Soviet Union" "Sweden" "Malaysia"
## [49] "Ireland" "Luxembourg" "Austria"
## [52] "Peru" "Senegal" "Switzerland"
## [55] "Ghana" "Saudi Arabia" "Armenia"
## [58] "Jordan" "Mongolia" "Namibia"
## [61] "Finland" "Lebanon" "Qatar"
## [64] "Vietnam" "Russia" "Malta"
## [67] "Kuwait" "Czech Republic" "Bahamas"
## [70] "Sri Lanka" "Cayman Islands" "Bangladesh"
## [73] "Zimbabwe" "Hungary" "Latvia"
## [76] "Liechtenstein" "Venezuela" "Morocco"
## [79] "Cambodia" "Albania" "Nicaragua"
## [82] "Greece" "Croatia" "Guatemala"
## [85] "West Germany" "Slovenia" "Dominican Republic"
## [88] "Nepal" "Samoa" "Bermuda"
## [91] "Ecuador" "Georgia" "Botswana"
## [94] "Iraq" "Vatican City" "Angola"
## [97] "Jamaica" "Kazakhstan" "Malawi"
## [100] "Slovakia" "Lithuania" "Afghanistan"
## [103] "Paraguay" "Somalia" "Sudan"
## [106] "Panama" "Uganda" "East Germany"
## [109] "Ukraine" "Montenegro"length(unique(genre_country$country))## [1] 110movie_year <- genre_country %>%
# mutate(date_added = as.numeric(date_added)) %>%
filter(country == "United States" |
country == "India"|
country == "United Kingdom"|
country == "Canada"|
country == "France") %>%
group_by(country, date_added) %>%
summarise(count = n())
movie_year## # A tibble: 45 x 3
## # Groups: country [5]
## country date_added count
## <chr> <chr> <int>
## 1 Canada 2013 1
## 2 Canada 2014 2
## 3 Canada 2015 5
## 4 Canada 2016 28
## 5 Canada 2017 116
## 6 Canada 2018 125
## 7 Canada 2019 125
## 8 Canada 2020 149
## 9 Canada 2021 10
## 10 France 2011 2
## # … with 35 more rows#bar <- ggplot(genre_5_country, aes(fill=listed_in, y=count, x=country)) +
# geom_bar(position="fill", stat="identity")
#bar
chart <- ggplot(movie_year, aes(x = date_added, y = count, group = country, color = country)) +
geom_line() +
labs(x = "Year", y = "Count", fill = "Country", title = "Number of movies by Year in Top 5 Countries")
chart <- ggplotly(chart)
chartNetflix movie genre trends by year
장르 (‘listed in’ column) 별로 몇년도에 넷플에 애드 됐는지? (release year아니고 넷플 애드된 해) 몇년도에 어떤 장르가 제일 많이 애드됐는지
head(movie$listed_in,10) # each movie has multiple genres separated by commas## [1] "Dramas, International Movies"
## [2] "Horror Movies, International Movies"
## [3] "Action & Adventure, Independent Movies, Sci-Fi & Fantasy"
## [4] "Dramas"
## [5] "Horror Movies, International Movies"
## [6] "Dramas"
## [7] "Horror Movies, International Movies"
## [8] "Horror Movies, International Movies, Thrillers"
## [9] "Dramas, Thrillers"
## [10] "Documentaries, International Movies, Sports Movies"movie_genre <- movie %>%
mutate(listed_in = strsplit(as.character(listed_in), ", ")) %>% # split genre column by comma
unnest(listed_in) %>%
select("title","date_added","listed_in")
movie_genre## # A tibble: 11,546 x 3
## title date_added listed_in
## <chr> <chr> <chr>
## 1 7:19 2016 Dramas
## 2 7:19 2016 International Movies
## 3 23:59 2018 Horror Movies
## 4 23:59 2018 International Movies
## 5 9 2017 Action & Adventure
## 6 9 2017 Independent Movies
## 7 9 2017 Sci-Fi & Fantasy
## 8 21 2020 Dramas
## 9 122 2020 Horror Movies
## 10 122 2020 International Movies
## # … with 11,536 more rowsunique(movie_genre$listed_in) # all Netflix genre## [1] "Dramas" "International Movies"
## [3] "Horror Movies" "Action & Adventure"
## [5] "Independent Movies" "Sci-Fi & Fantasy"
## [7] "Thrillers" "Documentaries"
## [9] "Sports Movies" "Comedies"
## [11] "Romantic Movies" "Movies"
## [13] "Music & Musicals" "LGBTQ Movies"
## [15] "Faith & Spirituality" "Children & Family Movies"
## [17] "Classic Movies" "Cult Movies"
## [19] "Stand-Up Comedy" "Anime Features"movie_genre <- movie_genre %>%
group_by(date_added, listed_in) %>%
summarise(count = n()) # count number of genres by year
movie_genre## # A tibble: 165 x 3
## # Groups: date_added [14]
## date_added listed_in count
## <chr> <chr> <int>
## 1 2008 Dramas 1
## 2 2008 Independent Movies 1
## 3 2008 Thrillers 1
## 4 2009 Dramas 1
## 5 2009 Horror Movies 1
## 6 2009 International Movies 1
## 7 2010 Cult Movies 1
## 8 2010 Horror Movies 1
## 9 2011 Children & Family Movies 1
## 10 2011 Dramas 13
## # … with 155 more rows# stacked bar chart
bar <- ggplot(movie_genre, aes(fill=listed_in, y=count, x=date_added)) +
geom_bar(position="stack", stat="identity") +
labs (x = "Year", fill = "Genre", title = "Genre by Year")
bar <- ggplotly(bar)
bar # interactive로 해서 한 장르의 시간별 변화도 관찰 가능# percent stacked bar chart
bar <- ggplot(movie_genre, aes(fill=listed_in, y=count, x=date_added)) +
geom_bar(position="fill", stat="identity") +
labs (x = "Year", y = "Percent", fill = "Genre", title = "Genre by Year")
bar
마지막 percent stacked barchart를 보면 해가 지날수록 장르가 더 다양해 지는 걸 볼 수 있었음 2014년을 기점으로 international과 LGBTQ movie의 비중이 대폭 늘어남 전체적으로 시간이 지날 수록 영화의 다양성에 집중하고자 하는 양상이 드러남
Netflix genre trends by country
#넷플 영화 갯수 탑 5 나라의 장르 비율
#genre_5_country <- genre_country %>%
# filter(country == "United States" |
# country == "India"|
# country == "United Kingdom"|
# country == "Canada"|
# country == "France") %>%
# group_by(country, listed_in) %>%
# summarise(count = n())
# stacked bar chart
#bar <- ggplot(genre_5_country, aes(fill=listed_in, y=count, x=country)) +
# geom_bar(position="fill", stat="identity")
#bar
# 각 나라 별 도넛 차트 for genre
# https://homepage.divms.uiowa.edu/~luke/classes/STAT4580/catone.html
#pie <- ggplot(genre_5_country) +
# geom_col(aes(x = 1, y = count, fill = listed_in), position = "fill") +
# coord_polar(theta = "y") +
# labs(title = "Movie Genres", fill = "Genre") +
# facet_wrap(~ country) +
# theme_bw() +
# theme(axis.title = element_blank(), # remove axis and grid lines
# axis.text = element_blank(),
# axis.ticks = element_blank(),
# panel.grid.major = element_blank(),
# panel.grid.minor = element_blank(),
# panel.border = element_blank())
#pie <- pie + xlim(0, 1.5)
#pie
# 영화 갯수 제일 많은 미국 한 나라의 장르 비율 도넛 차트
genre_usa <- genre_country %>%
filter(country == "United States") %>%
group_by(country, listed_in) %>%
summarise(count = n()) %>%
arrange(desc(count))
# donut chart 만들기 (많은순으로 정렬)
pie <- ggplot(genre_usa) +
geom_col(aes(x = 1, y = count, fill = reorder(listed_in,count)), position = "fill") +
coord_polar(theta = "y", start = 0) +
labs(title = "Movie Genres in United States", fill = "Genre") +
guides(fill = guide_legend(reverse = TRUE)) +
facet_wrap(~ country) +
theme_bw() +
theme(axis.title = element_blank(), # remove axis and grid lines
axis.text = element_blank(),
axis.ticks = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank())
pie <- pie + xlim(0, 1.5)
pie
Very small portion of American movies are categorized as international movies. <- dㅣ건 미국 도넛차트만 둘거면 빼야할듯